Module Details

Module Code: MANU9002
Title: Lean Sigma - Advanced Stats
Long Title: Lean Sigma - Advanced Statistical Tools for Process Optimisation
NFQ Level: Expert
Valid From: Semester 1 - 2011/12 ( September 2011 )
Duration: 1 Semester
Credits: 5
Field of Study: 5400 - Manufacturing Engineering
Module Delivered in: 3 programme(s)
Module Description: This module is primarily aimed at giving students the advanced mathematical tools required in a Lean Sigma environment. How these tools are used within a lean sigma culture is explained. The module gives a detailed treatment of the appropriate advanced statistical techniques as applied in a modern business environment
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Synthesise information in relation to approaches to Lean Sigma and distinguish between different approaches to variability reduction and waste elimination
LO2 Conduct, analyse and interpret Value Stream Mapping exercises
LO3 Design, analyse and interpret the output from experiments, using DOE methodologies.
LO4 Choose a set of one or more tools from a set of advanced statistical tools, and apply the set to the analysis of complex optimization problems.
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named MTU module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
Requirements

This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
No requirements listed
 
Indicative Content
Lean Sigma Methodologies
Lean principles and Six Sigma methodology. Value and foundations of lean and six sigma. Six Sigma and Lean applications in manufacturing, service, design and innovation. Variability reduction and waste elimination. The DMAIC framework.
Value Stream Mapping
Value Stream for Process Analysis. Flow charts/Process mapping. Analyse current state and map, plan future state and map, implementation planning.
Design of Experiments
Terminology and logic of hypothesis testing; null and alternative hypotheses, test statistic, reference distribution, p-value and its implications. Particular emphasis on t-tests and F-tests. Assumptions underlying tests, parametric versus non-parametric tests. ANOVA: one-way, two-way and general factorial models. Full and fractional factorials, confounding and design resolution.
Advanced Statistical Techniques
Establish and interpret regression equations, both simple and multiple; conduct hypothesis tests to establish the significance level of the model. Use multivariate tools such as principal components analysis, factor analysis, discriminant analysis, MANOVA, multi-vari charts. Response surface methods, process robustness, EVOP.
Module Content & Assessment
Assessment Breakdown%
Coursework40.00%
End of Module Formal Examination60.00%

Assessments

Coursework
Assessment Type Short Answer Questions % of Total Mark 20
Timing Week 4 Learning Outcomes 1,2
Assessment Description
Assess ability to synthesise information in relation to approaches to lean/six sigma and distinguish between different approaches to variability reduction and waste elimination.
Assessment Type Other % of Total Mark 20
Timing Week 8 Learning Outcomes 3,4
Assessment Description
Independently worked assignments allocated in week 8 to be submitted in week 12
End of Module Formal Examination
Assessment Type Formal Exam % of Total Mark 60
Timing End-of-Semester Learning Outcomes 1,3,4
Assessment Description
End-of-Semester Final Examination
Reassessment Requirement
Repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.

The University reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact No Description Every Week 3.00 3
Independent & Directed Learning (Non-contact) Non Contact No Description Every Week 4.00 4
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 3.00
This module has no Part Time workload.
 
Module Resources
Recommended Book Resources
  • Douglas C. Montgomery, George C. Runger.. (2011), Applied Statistics and Probability for Engineers, 5th. Hoboken, NJ : John Wiley & Sons, [ISBN: 978-0-470-50578-6].
  • Douglas C. Montgomery. (2009), Design and analysis of experiments, 7th. Wiley, Hoboken, N.J., [ISBN: 978-0-470-39882-1].
  • Forrest W. Breyfogle III. (2003), Implementing Six Sigma, 2nd. John Wiley & Sons, Hoboken, N.J., [ISBN: 978-0-471-26572-6].
Supplementary Book Resources
  • Rajesh Jugulum, Philip Samuel,. (2008), Design for Lean Six Sigma, 1st. [ISBN: 978-0-470-00751-8].
  • George E. P. Box, J. Stuart Hunter, William G. Hunter. (2005), Statistics for experimenters, Wiley-Interscience, Hoboken, N.J., [ISBN: 0-471-71813-0].
This module does not have any article/paper resources
Other Resources
  • Manual plus CD-Rom, Quality Council of Indiana. (2007), Certified Six Sigma Black Belt Primer, Electronic Exam and Solution Text, W. Terre Haute, IN 47885-1124, Quality Council of Indiana,
    http://www.qualitycouncil.com
  • Statistical Software, Students will have access to Minitab statistical software in the mathematics laboratory..
 
Module Delivered in
Programme Code Programme Semester Delivery
CR_ECHBI_9 Master of Engineering in Chemical and Biopharmaceutical Engineering 1 Mandatory
CR_EMENG_9 Master of Engineering in Mechanical Engineering 1 Mandatory
CR_EPIAI_9 Postgraduate Certificate in Process Industries Advancements and Innovation 1 Mandatory